OUTSTAT= Data Set

The OUTSTAT= data set is similar to the TYPE=CORR data set that the CORR procedure produces. The following table relates the
TYPE= value for the OUTSTAT= data set to the options that are specified in the PROC HPPRINCOMP
statement:

the variables that are analyzed (that is, those in the VAR
statement); or, if there is no VAR statement, all numeric variables not listed in any other statement; or, if there is a
PARTIAL
statement, the residual variables as described in the section "OUT= Data Set"

Each observation in the new data set contains some type of statistic, as indicated by the _TYPE_ variable. The values of the _TYPE_ variable are as follows:

_TYPE_

Contents

MEAN

mean of each variable. If you specify the PARTIAL statement, this observation is omitted.

STD

standard deviations. If you specify the COV option, this observation is omitted, so the SCORE procedure does not standardize
the variables before computing scores. If you use the PARTIAL statement, the standard deviation of a variable is computed
as its root mean squared error as predicted from the PARTIAL variables.

USTD

uncorrected standard deviations. When you specify the NOINT option in the PROC HPPRINCOMP statement, the OUTSTAT= data set
contains standard deviations not corrected for the mean. However, if you also specify the COV option in the PROC HPPRINCOMP
statement, this observation is omitted.

N

number of observations on which the analysis is based. This value is the same for each variable. If you specify the PARTIAL
statement and the value of the VARDEF=
option is DF or unspecified, then the number of observations is decremented by the degrees of freedom for the PARTIAL variables.

SUMWGT

the sum of the weights of the observations. This value is the same for each variable. If you specify the PARTIAL statement
and VARDEF=WDF, then the sum of the weights is decremented by the degrees of freedom for the PARTIAL variables. This observation
is output only if the value is different from that in the observation for which _TYPE_=‘N’.

CORR

correlations between each variable and the variable specified by the _NAME_ variable. The number of observations for which _TYPE_=‘CORR’ is equal to the number of variables being analyzed. If you specify the COV option, no _TYPE_=‘CORR’ observations are produced. If you use the PARTIAL statement, the partial correlations, not the raw correlations, are
output.

UCORR

uncorrected correlation matrix. When you specify the NOINT option without the COV option in the PROC HPPRINCOMP statement,
the OUTSTAT= data set contains a matrix of correlations not corrected for the means. However, if you also specify the COV
option in the PROC HPPRINCOMP statement, this observation is omitted.

COV

covariances between each variable and the variable specified by the _NAME_ variable. _TYPE_=‘COV’ observations are produced only if you specify the COV option. If you use the PARTIAL statement, the partial covariances,
not the raw covariances, are output.

UCOV

uncorrected covariance matrix. When you specify the NOINT and COV options in the PROC HPPRINCOMP statement, the OUTSTAT= data
set contains a matrix of covariances not corrected for the means.

EIGENVAL

eigenvalues. If the N= option requests less than the maximum number of principal components, only the specified number of
eigenvalues are produced, with missing values filling out the observation.

SCORE

eigenvectors. The _NAME_ variable contains the name of the corresponding principal component as constructed from the PREFIX= option. The number of
observations for which _TYPE_=‘SCORE’ equals the number of principal components computed. The eigenvectors have unit length unless you specify the STD
option, in which case the unit-length eigenvectors are divided by the square roots of the eigenvalues to produce scores that
have unit standard deviations.

To obtain the principal component scores, if the COV option is not specified, these coefficients should be multiplied by the
standardized data. For the COV option, these coefficients should be multiplied by the centered data. To center and standardize
the data, you should use means that are obtained from the observation for which _TYPE_=’MEAN’ and standard deviations that are obtained from the observation for which _TYPE_=’STD’.

USCORE

scoring coefficients to be applied without subtracting the mean from the raw variables. Observations for which _TYPE_=‘USCORE’ are produced when you specify the NOINT option in the PROC HPPRINCOMP statement.

To obtain the principal component scores, these coefficients should be multiplied by the data that are standardized by the
uncorrected standard deviations obtained from the observation for which _TYPE_=’USTD’.

RSQUARED

R squares for each VAR variable as predicted by the PARTIAL variables.

B

regression coefficients for each VAR variable as predicted by the PARTIAL variables. This observation is produced only if
you specify the COV option.

STB

standardized regression coefficients for each VAR variable as predicted by the PARTIAL variables. If you specify the COV option,
this observation is omitted.

You can use the data set in the SCORE procedure to compute principal component scores, or you can use it as input to the FACTOR
procedure and specify METHOD=SCORE to rotate the components. If you use the PARTIAL statement, the scoring coefficients should
be applied to the residuals, not to the original variables.